Algorithms for massive datasets2026/27
The course aims at describing the big data processing framework, both in terms of methodologies and technologies.
Expected results
Students:
- will be able to use technologies for the distributed storage of datasets;
- will know the MapReduce distributed processing framework and its leading extensions;
- will know the principal algorithms used in order to deal with classical big data problems, as well as to implement them using a distributed processing framework;
- will be able to choose appropriate methods for solving big data problems.
News
No announcements yet.
Course schedule
Lectures take place in presence, during the spring semester, at the "Città studi" educational sector. The schedule is as follows:
| Day | Hour | Place |
|---|---|---|
| Tuesday | 13:30 - 16:30 | Lab. magistrale 5 piano |
| Thursday | 13:30 - 16:30 | Lab. magistrale 5 piano |
Any change to the schedule will be announced in class and published in paragraph News of this page.
Office hours
By appointment, room 5015 of the Computer Science Department.
It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.
Course material
Lectures are based:
- on the textbook Mining of Massive Datasets, written by A. Rajaraman and J. Ullman (marked by RU in the calendar of lectures), available as a free download in the authors' Web site and published in hardcopy by Cambridge University Press (ISBN:9781107015357);
- on the notes and sample code published in the calendar of lectures.
Syllabus
The course explains the topics listed in the lecture calendar (available at the beginning of the course), covering the textbook contents as well as the contents of the remaining documents listed in Course material.
Prereqs
The course requires knowledge of the main topics of bachelor-level computer programming, calculus, probability, and statistics.
Lectures calendar
| Lecture | Date | Topic | Material |
|---|---|---|---|
| L01 | 12/01/2027 | HDFS and map-reduce. | |
| L02 | 14/01/2027 | Map-reduce algorithms. Communication cost model. | |
| L03 | 19/01/2027 | Link analysis. | |
| L04 | 21/01/2027 | Link analysis. Spark. | |
| L05 | 26/01/2027 | Finding similar items. | |
| L06 | 28/01/2027 | Finding similar items. | |
| L07 | 02/02/2027 | Frequent itemsets. | |
| L08 | 04/02/2027 | Frequent itemsets. | |
| L09 | 09/02/2027 | Mining data streams. | |
| L10 | 11/02/2027 | Clustering. | |
| L11 | 16/02/2027 | Regression. | |
| L12 | 18/02/2027 | Logistic regression. Recommendation systems. | |
| L13 | 23/02/2027 | Recommendation systems. | |
| L14 | 25/02/2027 | Deep learning and TensorFlow. | |
| L15 | 02/03/2027 | Transformers and LLMs. | |
| L16 | 04/03/2027 | Dimensionality reduction. Gradient boosting. |
Exam modalities
The exam consists of a written test comprising open-ended theoretical or applied questions on the topics covered during the course, with a level of difficulty consistent with the teaching activities. During the test, students may consult a formula sheet provided by the instructor and use non-programmable calculators. Textbooks, personal notes and other materials are not permitted, nor are electronic devices other than the permitted calculators.
The written test is marked out of 30, with a minimum passing mark of 18/30, and results are communicated by email. Assessment takes into account students' mastery of the topics covered, the correctness of their methods and proposed solutions, their correct use of mathematical notation and formalism, and their appropriate use of technical terminology.
Based on the results of the written test, the instructor may require an additional oral examination to further assess the student's understanding of the topics covered and overall level of preparation. The final mark, expressed out of 30, is determined on the basis of the written test and, where required, the oral examination.
Exam sessions
| Session | Date |
|---|---|
| March | 30/03/27 |
| June | 18/06/27 |
| July | 07/07/27 |
| September | 13/09/27 |
| November | N/A |
| December | N/A |